Forest floor depths and fuel loads in upland Canadian forests
Bibliographic record
Abstract
Forest floor data are important for many forest resource management applications. In terms of fire and forest carbon dynamics, these data are critical for modeling direct carbon emissions from wildfire in Canadian forests because forest floor organic material is usually the greatest emissions source. However, there are very few data available to initialize wildfire emission models. Six data sets representing 41 534 forest stands across Canada were combined to provide summary statistics and to analyze factors controlling forest floor fuel loads and depths. The impacts of dominant tree species, ecozone, drainage-class, and age-class data on forest floor fuel loads and depth were examined using ANOVA and regression. All four parameters were significant factors affecting forest floor fuel load and depth, but only tree species and ecozone were substantially influential. Although forest floor depths summarized in this study are similar to those of previous studies, forest floor fuel loads are higher. Average forest floor fuel loads and depths are summarized by species and ecozone and can be used to initialize dynamic stand-level forest models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".